Fast assimilation of frequently acquired 4D seismic data for reservoir history matching

The study in this paper proposed a new framework for history matching of frequently acquired 4D seismic data. To achieve this goal, the large volumes of seismic data from the many repeated 4D monitors are firstly condensed into a single attribute by directly correlating them to the reservoir production and injection performances. This ‘well2seis’ cross-correlation is achieved by defining a linear relationship between pressure and saturation-related 4D seismic responses and the cumulative changes of reservoir fluid volumes derived from wells. It is shown that such a cross-disciplinary attribute not only reduces the amount of 4D seismic data for history matching, but also enhances the seismic data reliability since the 4D seismic observations are conditioned by low-uncertainty production data from reservoir engineering domain. In the second step, Morris sensitivity analysis is adapted to fast diagnose the uncertainty reservoir model parameters that are sensitive to the well2seis attributes. To quantitatively assimilate the well2seis observations to calibrate these uncertainty parameters, we then proposed a well2seis objective function that quantifies the mismatch between the observed and model simulated well2seis attributes. Ensemble Smoother with Multiple Data Assimilation (ES-MDA) is performed at the end to iteratively assimilate the well2seis observations by minimizing the well2seis misfit. Application of the proposed workflow to a North Sea field case shows that, when history matching to the observed well2seis attribute that honours the information from seismic and reservoir engineering domains, it can significantly reduce the uncertainty of key reservoir parameters, hence improving the matching quality to both 4D seismic and production observations and enhancing the prediction reliability of the reservoir models. Compared to traditional history matching approaches that attempt to match individual seismic time-lapse attributes and production observations, this approach is observed to significantly boost the history matching efficiency by reducing the number of time-consuming iterations.